arXiv:2608.17286v1 Announce Type: new Abstract: Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic scaling law study for text-to-image diffusion models using Abra, a controlled f…
Although text-to-image diffusion models exhibit remarkable generative power, concept erasure techniques are essential for preventing harmful content. Existing adversarial probes evaluate these methods by testing whether erased concepts can still be recovered. However, existing er…
Scaling laws for text-to-image diffusion models reveal predictable compute-optimal training requiring far more data per parameter than language models, with robust overtraining behavior and universal curve shapes.
Researchers propose a latent-to-pixel training strategy that accelerates convergence and improves inference speed for large-scale pixel-space diffusion models.
arXiv:2608.15705v1 Announce Type: new Abstract: Controllable text-to-image diffusion models can often follow the global layout of spatial conditions, yet still violate fine-grained structures such as object boundaries, thin contours, and medium/small conditioned regions. This lim…
arXiv cs.CV
TIER_1English(EN)·Dengyang Jiang, Ruoyi Du, Zhennan Chen, Dongyang Liu, Zanyi Wang, Mingzhe Zheng, Xiangpeng Yang, Huanqia Cai, Aiming Hao, Yuming Jiang, Peng Gao, Harry Yang, Steven Hoi·
arXiv:2608.16887v1 Announce Type: new Abstract: This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on small-scale or class-conditional settings. Consequently,…
